| Full text | |
| Author(s): |
Cutigi, Jorge Francisco
[1, 2]
;
Evangelista, Adriane Feijo
[3]
;
Reis, Rui Manuel
[3]
;
Simao, Adenilso
[1]
Total Authors: 4
|
| Affiliation: | [1] Univ Sao Paulo, Sao Carlos, SP - Brazil
[2] Fed Inst Sao Paulo, Sao Carlos, SP - Brazil
[3] Barretos Canc Hosp, Mol Oncol Res Ctr, Barretos, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | SCIENTIFIC REPORTS; v. 11, n. 1 DEC 7 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Identifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |